Learned Block-based Hybrid Image Compression
- URL: http://arxiv.org/abs/2012.09550v3
- Date: Mon, 18 Jan 2021 11:34:29 GMT
- Title: Learned Block-based Hybrid Image Compression
- Authors: Yaojun Wu, Xin Li, Zhizheng Zhang, Xin Jin, Zhibo Chen
- Abstract summary: Recent works on learned image compression perform encoding and decoding processes in a full-resolution manner.
Full-resolution inference often causes the out-of-memory(OOM) problem with limited GPU resources.
This paper provides a learned block-based hybrid image compression framework.
- Score: 33.44942603425436
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recent works on learned image compression perform encoding and decoding
processes in a full-resolution manner, resulting in two problems when deployed
for practical applications. First, parallel acceleration of the autoregressive
entropy model cannot be achieved due to serial decoding. Second,
full-resolution inference often causes the out-of-memory(OOM) problem with
limited GPU resources, especially for high-resolution images. Block partition
is a good design choice to handle the above issues, but it brings about new
challenges in reducing the redundancy between blocks and eliminating block
effects. To tackle the above challenges, this paper provides a learned
block-based hybrid image compression (LBHIC) framework. Specifically, we
introduce explicit intra prediction into a learned image compression framework
to utilize the relation among adjacent blocks. Superior to context modeling by
linear weighting of neighbor pixels in traditional codecs, we propose a
contextual prediction module (CPM) to better capture long-range correlations by
utilizing the strip pooling to extract the most relevant information in
neighboring latent space, thus achieving effective information prediction.
Moreover, to alleviate blocking artifacts, we further propose a boundary-aware
postprocessing module (BPM) with the edge importance taken into account.
Extensive experiments demonstrate that the proposed LBHIC codec outperforms the
VVC, with a bit-rate conservation of 4.1%, and reduces the decoding time by
approximately 86.7% compared with that of state-of-the-art learned image
compression methods.
Related papers
- Multi-Scale Invertible Neural Network for Wide-Range Variable-Rate Learned Image Compression [90.59962443790593]
In this paper, we present a variable-rate image compression model based on invertible transform to overcome limitations.
Specifically, we design a lightweight multi-scale invertible neural network, which maps the input image into multi-scale latent representations.
Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared to existing variable-rate methods.
arXiv Detail & Related papers (2025-03-27T09:08:39Z) - Continuous Patch Stitching for Block-wise Image Compression [56.97857167461269]
We propose a novel continuous patch stitching (CPS) framework for block-wise image compression.
Our CPS framework achieves the state-of-the-art performance against existing baselines, whilst requiring less than half of computing resources of existing models.
arXiv Detail & Related papers (2025-02-24T03:11:59Z) - Exploiting Inter-Image Similarity Prior for Low-Bitrate Remote Sensing Image Compression [10.427300958330816]
We propose a codebook-based RS image compression (Code-RSIC) method with a generated discrete codebook.
The code significantly outperforms state-of-the-art traditional and learning-based image compression algorithms in terms of perception quality.
arXiv Detail & Related papers (2024-07-17T03:33:16Z) - HybridFlow: Infusing Continuity into Masked Codebook for Extreme Low-Bitrate Image Compression [51.04820313355164]
HyrbidFlow combines the continuous-feature-based and codebook-based streams to achieve both high perceptual quality and high fidelity under extreme lows.
Experimental results demonstrate superior performance across several datasets under extremely lows.
arXiv Detail & Related papers (2024-04-20T13:19:08Z) - MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model [78.4051835615796]
This paper proposes a method called Multimodal Image Semantic Compression.
It consists of an LMM encoder for extracting the semantic information of the image, a map encoder to locate the region corresponding to the semantic, an image encoder generates an extremely compressed bitstream, and a decoder reconstructs the image based on the above information.
It can achieve optimal consistency and perception results while saving perceptual 50%, which has strong potential applications in the next generation of storage and communication.
arXiv Detail & Related papers (2024-02-26T17:11:11Z) - Extreme Image Compression using Fine-tuned VQGANs [43.43014096929809]
We introduce vector quantization (VQ)-based generative models into the image compression domain.
The codebook learned by the VQGAN model yields a strong expressive capacity.
The proposed framework outperforms state-of-the-art codecs in terms of perceptual quality-oriented metrics.
arXiv Detail & Related papers (2023-07-17T06:14:19Z) - You Can Mask More For Extremely Low-Bitrate Image Compression [80.7692466922499]
Learned image compression (LIC) methods have experienced significant progress during recent years.
LIC methods fail to explicitly explore the image structure and texture components crucial for image compression.
We present DA-Mask that samples visible patches based on the structure and texture of original images.
We propose a simple yet effective masked compression model (MCM), the first framework that unifies LIC and LIC end-to-end for extremely low-bitrate compression.
arXiv Detail & Related papers (2023-06-27T15:36:22Z) - Improving Multi-generation Robustness of Learned Image Compression [16.86614420872084]
We show that LIC can achieve comparable performance to the first compression of BPG even after 50 times reencoding without any change of the network structure.
arXiv Detail & Related papers (2022-10-31T03:26:11Z) - Implicit Neural Representations for Image Compression [103.78615661013623]
Implicit Neural Representations (INRs) have gained attention as a novel and effective representation for various data types.
We propose the first comprehensive compression pipeline based on INRs including quantization, quantization-aware retraining and entropy coding.
We find that our approach to source compression with INRs vastly outperforms similar prior work.
arXiv Detail & Related papers (2021-12-08T13:02:53Z) - Modeling Lost Information in Lossy Image Compression [72.69327382643549]
Lossy image compression is one of the most commonly used operators for digital images.
We propose a novel invertible framework called Invertible Lossy Compression (ILC) to largely mitigate the information loss problem.
arXiv Detail & Related papers (2020-06-22T04:04:56Z) - Learning End-to-End Lossy Image Compression: A Benchmark [90.35363142246806]
We first conduct a comprehensive literature survey of learned image compression methods.
We describe milestones in cutting-edge learned image-compression methods, review a broad range of existing works, and provide insights into their historical development routes.
By introducing a coarse-to-fine hyperprior model for entropy estimation and signal reconstruction, we achieve improved rate-distortion performance.
arXiv Detail & Related papers (2020-02-10T13:13:43Z) - A Unified End-to-End Framework for Efficient Deep Image Compression [35.156677716140635]
We propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies.
Specifically, we design an auto-encoder style network for learning based image compression.
Our EDIC method can also be readily incorporated with the Deep Video Compression (DVC) framework to further improve the video compression performance.
arXiv Detail & Related papers (2020-02-09T14:21:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.